SoroushVahidi/scidocs huggingface dataset:
Multiple Kernel Fuzzy Clustering
(2012)

While fuzzy c-means is a popular soft-clustering method, its effectiveness is largely limited to spherical clusters. By applying kernel tricks, the kernel fuzzy c-means algorithm attempts to address this problem by mapping data with nonlinear relationships to appropriate feature spaces. Kernel combination, or selection, is crucial for effective kernel clustering. Unfortunately, for most applications, it is uneasy to find the right combination. We propose a multiple kernel fuzzy c-means (MKFC) algorithm that extends the fuzzy c-means algorithm with a multiple kernel-learning setting. By incorporating multiple kernels and automatically adjusting the kernel weights, MKFC is more immune to ineffective kernels and irrelevant features. This makes the choice of kernels less crucial. In addition, we show multiple kernel k-means to be a special case of MKFC. Experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed MKFC algorithm.
Helicopter induced propeller injuries.
(2015)

Following routine maintenance a military helicopter was returned to service. Due to negligence some hoses had not been connected correctly. The wrongly connected hoses resulted in a malfunction, causing the rotor blades of the helicopter to smash through the windows of the helicopter cockpit. Both the pilot and the co-pilot were hit by the rotor blades and decapitated. Other passengers in the helicopter were injured. Due to vibrations the helicopter fell to the ground and its tail partly broke away (Figs. 1, 2). The bodies and body parts of the pilot and co-pilot were found on the ground outside the helicopter cockpit.
Sonoliards: Rendering Audible Sound Spots by Reflecting the Ultrasound Beams
(2017)

This paper proposes a dynamic acoustic field generation system for a spot audio towards particular person indoors. Spot audio techniques have been explored by generating the ultrasound beams toward the target person in certain area however everyone in this area can hear the sound. Our system recognizes the positions of each person indoor using motion capture and 3D model data of the room. After that we control direction of parametric speaker in real-time so that sound reach only particular person by calculating the reflection of sound on surfaces such as wall and ceiling. We calculate direction of parametric speaker using a beam tracing method. We present generating methods of dynamic acoustic field in our system and conducted the experiments on human factor to evaluate performance of proposed system.
Emotions , Partisanship , and Misperceptions : How Anger and Anxiety Moderate the Effect of Partisan Bias on Susceptibility to Political Misinformation
(2015)

Citizens are frequently misinformed about political issues and candidates but the circumstances under which inaccurate beliefs emerge are not fully understood. This experimental study demonstrates that the independent experience of two emotions, anger and anxiety, in part determines whether citizens consider misinformation in a partisan or open-minded fashion. Anger encourages partisan, motivated evaluation of uncorrected misinformation that results in beliefs consistent with the supported political party, while anxiety at times promotes initial beliefs based less on partisanship and more on the information environment. However, exposure to corrections improves belief accuracy, regardless of emotion or partisanship. The results indicate that the unique experience of anger and anxiety can affect the accuracy of political beliefs by strengthening or attenuating the influence of partisanship.
Sensation seeking in England and America: cross-cultural, age, and sex comparisons.
(1978)

This study compared the factor structure of the Sensation-Seeking Scale (SSS) in English and American samples, and a new form of the SSS, applicable to both samples, was constructed. Three of the four factors showed good crossnational and cross-sex reliability. English and American males did not differ on the total SSS score, but American females scored higher than English females. Males in both countries scored higher than females on the total SSS score and on the Thrill and Adventure-Seeking and Disinhibition subscales. Significant age declines occurred for both sexes, particularly on Thrill and Adventure Seeking and Disinhibition.
A Better Baseline for AVA
(2018)

We introduce a simple baseline for action localization on the AVA dataset. The model builds upon the Faster R-CNN bounding box detection framework, adapted to operate on pure spatiotemporal features – in our case produced exclusively by an I3D model pretrained on Kinetics. This model obtains 21.9% average AP on the validation set of AVA v2.1, up from 14.5% for the best RGB spatiotemporal model used in the original AVA paper (which was pretrained on Kinetics and ImageNet), and up from 11.3% of the publicly available baseline using a ResNet101 image feature extractor, that was pretrained on ImageNet. Our final model obtains 22.8%/21.9% mAP on the val/test sets and outperforms all submissions to the AVA challenge at CVPR 2018.
Deep predictive policy training using reinforcement learning
(2017)

Skilled robot task learning is best implemented by predictive action policies due to the inherent latency of sensorimotor processes. However, training such predictive policies is challenging as it involves finding a trajectory of motor activations for the full duration of the action. We propose a data-efficient deep predictive policy training (DPPT) framework with a deep neural network policy architecture which maps an image observation to a sequence of motor activations. The architecture consists of three sub-networks referred to as the perception, policy and behavior super-layers. The perception and behavior super-layers force an abstraction of visual and motor data trained with synthetic and simulated training samples, respectively. The policy super-layer is a small subnetwork with fewer parameters that maps data in-between the abstracted manifolds. It is trained for each task using methods for policy search reinforcement learning. We demonstrate the suitability of the proposed architecture and learning framework by training predictive policies for skilled object grasping and ball throwing on a PR2 robot. The effectiveness of the method is illustrated by the fact that these tasks are trained using only about 180 real robot attempts with qualitative terminal rewards.
Piriformis syndrome, diagnosis and treatment.
(2009)

Piriformis syndrome (PS) is an uncommon cause of sciatica that involves buttock pain referred to the leg. Diagnosis is often difficult, and it is one of exclusion due to few validated and standardized diagnostic tests. Treatment for PS has historically focused on stretching and physical therapy modalities, with refractory patients also receiving anesthetic and corticosteroid injections into the piriformis muscle origin, belly, muscle sheath, or sciatic nerve sheath. Recently, the use of botulinum toxin (BTX) to treat PS has gained popularity. Its use is aimed at relieving sciatic nerve compression and inherent muscle pain from a tight piriformis. BTX is being used increasingly for myofascial pain syndromes, and some studies have demonstrated superior efficacy to corticosteroid injection. The success of BTX in treating PS supports the prevailing pathoanatomic etiology of the condition and suggests a promising future for BTX in the treatment of other myofascial pain syndromes.
A high-level 3D visualization API for Java and ImageJ
(2010)

Current imaging methods such as Magnetic Resonance Imaging (MRI), Confocal microscopy, Electron Microscopy (EM) or Selective Plane Illumination Microscopy (SPIM) yield three-dimensional (3D) data sets in need of appropriate computational methods for their analysis. The reconstruction, segmentation and registration are best approached from the 3D representation of the data set. Here we present a platform-independent framework based on Java and Java 3D for accelerated rendering of biological images. Our framework is seamlessly integrated into ImageJ, a free image processing package with a vast collection of community-developed biological image analysis tools. Our framework enriches the ImageJ software libraries with methods that greatly reduce the complexity of developing image analysis tools in an interactive 3D visualization environment. In particular, we provide high-level access to volume rendering, volume editing, surface extraction, and image annotation. The ability to rely on a library that removes the low-level details enables concentrating software development efforts on the algorithm implementation parts. Our framework enables biomedical image software development to be built with 3D visualization capabilities with very little effort. We offer the source code and convenient binary packages along with extensive documentation at http://3dviewer.neurofly.de .
Psychological first-aid: a practical aide-memoire.
(1995)

Despite advances made in recent years in medical first aid, psychiatric intervention, survival training and equipment design, many people still perish quickly during and immediately following a disastrous event. In this study, individuals and groups of survivors of life-threatening events were debriefed and the behavior of those who coped well during such a threat to life were compared with those who did not. The behaviors of those who coped well were distilled into a set of principles for psychological first aid; that is, a series of simple actions for use within a disaster which serves to recover victims to functional behavior as quickly as possible, thus increasing their chance for survival. These principles of psychological first aid have recently been introduced into basic first aid and survival training courses for both military and civilian units.
Iterative Thresholding Algorithm for Sparse Inverse Covariance Estimation
(2012)

The `1-regularized maximum likelihood estimation problem has recently become a topic of great interest within the machine learning, statistics, and optimization communities as a method for producing sparse inverse covariance estimators. In this paper, a proximal gradient method (G-ISTA) for performing `1-regularized covariance matrix estimation is presented. Although numerous algorithms have been proposed for solving this problem, this simple proximal gradient method is found to have attractive theoretical and numerical properties. G-ISTA has a linear rate of convergence, resulting in an O(log ε) iteration complexity to reach a tolerance of ε. This paper gives eigenvalue bounds for the G-ISTA iterates, providing a closed-form linear convergence rate. The rate is shown to be closely related to the condition number of the optimal point. Numerical convergence results and timing comparisons for the proposed method are presented. G-ISTA is shown to perform very well, especially when the optimal point is well-conditioned.
A gene-coexpression network for global discovery of conserved genetic modules.
(2003)

To elucidate gene function on a global scale, we identified pairs of genes that are coexpressed over 3182 DNA microarrays from humans, flies, worms, and yeast. We found 22,163 such coexpression relationships, each of which has been conserved across evolution. This conservation implies that the coexpression of these gene pairs confers a selective advantage and therefore that these genes are functionally related. Many of these relationships provide strong evidence for the involvement of new genes in core biological functions such as the cell cycle, secretion, and protein expression. We experimentally confirmed the predictions implied by some of these links and identified cell proliferation functions for several genes. By assembling these links into a gene-coexpression network, we found several components that were animal-specific as well as interrelationships between newly evolved and ancient modules.
Sparse inverse covariance estimation with the graphical lasso.
(2008)

We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm--the graphical lasso--that is remarkably fast: It solves a 1000-node problem ( approximately 500,000 parameters) in at most a minute and is 30-4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.
Anatomical and biomechanical mechanisms of subacromial impingement syndrome.
(2003)

Subacromial impingement syndrome is the most common disorder of the shoulder, resulting in functional loss and disability in the patients that it affects. This musculoskeletal disorder affects the structures of the subacromial space, which are the tendons of the rotator cuff and the subacromial bursa. Subacromial impingement syndrome appears to result from a variety of factors. Evidence exists to support the presence of the anatomical factors of inflammation of the tendons and bursa, degeneration of the tendons, weak or dysfunctional rotator cuff musculature, weak or dysfunctional scapular musculature, posterior glenohumeral capsule tightness, postural dysfunctions of the spinal column and scapula and bony or soft tissue abnormalities of the borders of the subacromial outlet. These entities may lead to or cause dysfunctional glenohumeral and scapulothoracic movement patterns. These various mechanisms, singularly or in combination may cause subacromial impingement syndrome.
Artificial Intelligence in Education
(2017)

In a life-long learning society, learning scenarios can be categorized into five types, which are “classroom learning”, “self-learning”, “inquiry learning”, “learning in doing” and “learning in working”. From a life-wide learning perspective, all these scenarios play vital roles for personal development. How to recognize these learning scenarios (including learning time, learning place, learning peers, learning activities, etc.) and provide the matched learning ways (including learning path, resources, peers, teachers, etc.) are the basis for smart learning environments, however few research could be found to address this problem. In order to solve this problem, we propose a conceptual framework of smart learning engine that is the core of integrated, interactive and intelligent (i) learning environments. The smart learning engine consists of three main functions. The first function is to identify data from student, teacher, subject area, and the environment using wireless sensors, the established learning resources and scenarios, and a learner modeling technology. The acquired data includes prior knowledge, theme-based context, leaner/teacher profile, physical environments, etc. The second function is to compute the best ways of learning based on the learning scenario and learning preference. In detail, this function includes modeling learner’s affective data, building knowledge structure, optimizing knowledge module, and connecting learners. The third function is to deploy personalized and adaptive strategy, resources and tools for students and teachers based on the computed results in the second function. Deploy interactive strategies, learning paces, learning resources, and delivery approaches are the core elements for this function.
Evolutionary reinforcement learning in FX order book and order flow analysis
(2003)

As macroeconomic fundamentals based modelling of FX timeseries have been shown not to fit the empirical evidence at horizons of less than one year, interest has moved towards microstructure-based approaches. Order flow data has recently been receiving an increasing amount of attention in equity market analyses and thus increasingly in foreign exchange as well. In this paper, order flow data is coupled with order book derived indicators and we explore whether pattern recognition techniques derived from computational learning can be applied to successfully infer trading strategies on the underlying timeseries. Due to the limited amount of data available the results are preliminary. However, the approach demonstrates promise and it is shown that using order flow and order book data is usually superior to trading on technical signals alone.
Enterprise Architecture for Digital Transformation
(2017)

Digital transformation requires an altogether new institutional logic and effective response at a requisite organizational level. Sensing and seizing fleeting market opportunities and reconfiguring the business in line with the shifting value proposition requires increasingly specialized resources, more dynamic capabilities, and in-built resilience in the face of change. While Enterprise Architecture (EA) has been suggested to facilitate enterprise transformation, the focus has traditionally been on process standardization and integration rather than on continuous adaptation to the changing business and technological landscape. For EA to have a desired impact, more adaptive conceptualizations of EA that address the requirements of the new digital environment are called for. In this conceptual paper, we explore the implications of digital transformation on enterprise architecture. In particular, we note that existing approaches to EA address integration and coherence within a single organization but typically fall short in the face of complexities pertaining to digital ecosystems. We suggest a vertical typology of organizational capabilities and postulate that today's digital environment increasingly requires adaptive capabilities that transcend the traditional notion of dynamic capabilities. We then investigate how EA can help build flexibility and resilience in the organization.
Looking under the bonnet : factors affecting student adoption of e-learning systems in
(2009)

The primary questions addressed in this paper are the following: what are the factors that affect students‟ adoption of an e-learning system and what are the relationships among these factors? This paper investigates and identifies some of the major factors affecting students‟ adoption of an e-learning system in a university in Jordan. E-learning adoption is approached from the information systems acceptance point of view. This suggests that a prior condition for learning effectively using e-learning systems is that students must actually use them. Thus, a greater knowledge of the factors that affect IT adoption and their interrelationships is a pre-cursor to a better understanding of student acceptance of e-learning systems. In turn, this will help and guide those who develop, implement, and deliver e-learning systems. In this study, an extended version of the Technology Acceptance Model (TAM) was developed to investigate the underlying factors that influence students‟ decisions to use an e-learning system. The TAM was populated using data gathered from a survey of 486 undergraduate students, who were using the Moodle based e-learning system at the Arab Open University. The model was estimated using Structural Equation Modelling (SEM). A path model was developed to analyze the relationships between the factors to explain students‟ adoption of the e-learning system. Whilst findings support existing literature about prior experience affecting perceptions, they also point to surprising group effects, which may merit future exploration.
Video semantic object segmentation by self-adaptation of DCNN
(2018)

This paper proposes a new framework for semantic segmentation of objects in videos. We address the label inconsistency problem of deep convolutional neural networks (DCNNs) by exploiting the fact that videos have multiple frames; in a few frames the object is confidently-estimated (CE) and we use the information in them to improve labels of the other frames. Given the semantic segmentation results of each frame obtained from DCNN, we sample several CE frames to adapt the DCNN model to the input video by focusing on specific instances in the video rather than general objects in various circumstances. We propose offline and online approaches under different supervision levels. In experiments our method achieved great improvement over the original model and previous state-of-the-art methods. c © 2016 Elsevier Ltd. All rights reserved.
Vision: towards real time epidemic vigilance through online social networks: introducing SNEFT -- social network enabled flu trends
(2010)

Our vision is to achieve faster and near real time detection and prediction of the emergence and spread of an influenza epidemic, through sophisticated data collection and analysis of Online Social Networks (OSNs) such as Facebook, MySpace, and Twitter. In particular, we present the design of a system called SNEFT (Social Network Enabled Flu Trends), which will be developed in a 12-month SBIR (Small Business Innovation Research) project funded by the National Institutes of Health (NIH). We describe the innovative technologies that will be developed in this project for collecting and aggregating OSN data, extracting information from it, and integrating it with mathematical models of influenza. One of the monitoring tools used by the Centers for Disease Control and Prevention (CDC) is reports of Influenza-Like Illness (ILI) cases; these reports are authoritative but typically have a delay of one to two weeks due to the largely manual process. We describe the SNEFT prototype in the context of predicting ILI cases well in advance of the CDC reports. We observe that OSN data is individually noisy but collectively revealing, and speculate on other applications that can potentially be enabled by OSN data collection and analysis.
Speaking in tongues: SQL access to NoSQL systems
(2014)

Non-relational data stores, which are usually called NoSQL systems, have become an important class of data management systems. They often outperform the relational systems. Yet there is no common way to interface with NoSQL systems. The Structured Query Language (SQL) has already proven to be useful to provide a uniform query language for all the relational systems. We identify a subset of SQL for access to NoSQL systems. Our extensible middleware translates SQL queries to the query languages of the connected NoSQL systems. The migration between these systems is thereby greatly simplified as well as the comparison of the supported NoSQL systems.
Upgrading Wireless Home Routers for Enabling Large-Scale Deployment of Cloudlets
(2015)

Smartphones become more and more popular over recent years due to their small form factors. However, such mobile systems are resource-constrained in view of computational power, storage and battery life. Offloading resource-intensive tasks (aka mobile cloud computing) to distant (e.g., cloud computing) or closely located data centers (e.g., cloudlet) overcomes these issues. Especially, cloudlets provide computational power with low latency for responsive applications due to their proximity to mobile users. However, a large-scale deployment of rangerestricted cloudlets is still an open challenge. In this paper, we propose a novel concept for a large-scale deployment of cloudlets by upgrading wireless home routers. Beside router’s native purpose of routing data packets through the network, it can now offer computing resources with low latency and high bandwidth without additional hardware. Proving our concept, we conducted comprehensive benchmark tests against existing concepts. As result, the feasibility of this concept is shown and provide a promising way to large-scale deploy cloudlets in existing infrastructures.